迈向个性化的人工智能交互 - 利用主观兴趣的神经签名来适应AI代理的行为
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest
AI总结：
本文提出通过神经签名检测用户兴趣，使深度强化学习AI代理适应个性化人类偏好，首次展示hBCI在虚拟环境中隐式强化AI控制系统的应用。
摘要：
强化学习AI通常使用环境中的客观奖励/惩罚信号（如游戏得分、完成时间等）来学习最优任务策略。然而，此类AI代理的人机交互应包含隐式且主观的强化信号（如人类对特定AI行为的偏好），以适应个体化的人类偏好。这种适应会模仿自然发生的增强信任和舒适度的社会互动过程。本文展示如何利用混合脑机接口（hBCI）检测个体在虚拟环境中的兴趣水平，以适应控制虚拟自动驾驶车辆的深度强化学习AI代理。具体而言，我们展示AI学习了一种保持与前车安全距离的驾驶策略，并最值得注意的是，当车辆乘客遇到感兴趣物体时，优先减速。这种适应使主观有趣物体的观看时间增加了20%。这是首次展示如何利用hBCI以包含用户偏好的方式向AI代理提供隐式强化。
英文摘要：
Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective -- e.g. human preferences for certain AI behavior -- in order to adapt the AI behavior to idiosyncratic human preferences. Such adaptations would mirror naturally occurring processes that increase trust and comfort during social interactions. Here, we show how a hybrid brain-computer-interface (hBCI), which detects an individual's level of interest in objects/events in a virtual environment, can be used to adapt the behavior of a Deep Reinforcement Learning AI agent that is controlling a virtual autonomous vehicle. Specifically, we show that the AI learns a driving strategy that maintains a safe distance from a lead vehicle, and most novelly, preferentially slows the vehicle when the human passengers of the vehicle encounter objects of interest. This adaptation affords an additional 20\% viewing time for subjectively interesting objects. This is the first demonstration of how an hBCI can be used to provide implicit reinforcement to an AI agent in a way that incorporates user preferences into the control system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
